Detecting long documents in a live camera feed

ABSTRACT

Aspects of the present disclosure provide methods and apparatuses for processing a digital image of a document, for example, to determine whether the document is a long document. An exemplary method generally includes obtaining a plurality of digital images of the document, determining a type of the document, loading one or more pre-defined metrics associated with the document based on the determined type of the document, determining one or more characteristics of the document based on one or more analyses performed on the plurality of digital images of the document, comparing the one or more characteristics of the document with the one or more pre-defined metrics, and determining the document to be a long document based, at least in part, on the comparison.

BACKGROUND Field

The present disclosure generally relates to processing digital images ofdocuments or forms. More specifically, the present disclosure providestechniques for detecting long documents in a live camera feed.

Related Art

In the course of using a mobile application, it is sometimes useful tocapture an image of a document. For example, a user of a financialmanagement application may capture an image of a receipt related to afinancial transaction for use within the application. In some cases,however, due to the limitations of cameras, such as restricted fields ofview, a document may be too long to capture in a single image ofsufficient quality to identify the document's textual content.

SUMMARY

Aspects of the present disclosure provide a computer-implemented methodfor processing a digital image of a document. The computer-implementedmethod generally includes obtaining a plurality of digital images of thedocument, determining a type of the document, loading one or morepre-defined metrics associated with the document based on the determinedtype of the document, determining one or more characteristics of thedocument based on one or more analyses performed on the plurality ofdigital images of the document, comparing the one or morecharacteristics of the document with the one or more pre-definedmetrics, and determining the document to be a long document based, atleast in part, on the comparison.

Another embodiment provides a non-transitory computer-readable storagemedium having instructions, which, when executed on a processor,performs an operation for processing a digital image of a document. Theoperation generally includes obtaining a plurality of digital images ofthe document, determining a type of the document, loading one or morepre-defined metrics associated with the document based on the determinedtype of the document, determining one or more characteristics of thedocument based on one or more analyses performed on the plurality ofdigital images of the document, comparing the one or morecharacteristics of the document with the one or more pre-definedmetrics, and determining the document to be a long document based, atleast in part, on the comparison.

Still another embodiment of the present invention includes a processorand a memory storing a instructions that, when executed on theprocessor, performs an operation for processing a digital image. Theoperation generally includes obtaining a plurality of digital images ofthe document, determining a type of the document, loading one or morepre-defined metrics associated with the document based on the determinedtype of the document, determining one or more characteristics of thedocument based on one or more analyses performed on the plurality ofdigital images of the document, and comparing the one or morecharacteristics of the document with the one or more pre-definedmetrics, and determining the document to be a long document based, atleast in part, on the comparison.

Aspects of the present disclosure provide a computer-implemented methodfor processing a digital image of a document. The computer-implementedmethod generally includes obtaining a plurality of digital images of thedocument, segmenting at least a first digital image of the plurality ofimages into pixels associated with a foreground of the first digitalimage and pixels associated with a background of the first digitalimage, detecting a plurality of contours in the segmented first digitalimage, deciding, for each detected contour of the plurality of contours,whether that contour is an open contour or a closed contour, anddetermining that one or more sides of the document is out-of-boundsbased, at least in part, on the decisions.

Another embodiment provides a non-transitory computer-readable storagemedium having instructions, which, when executed on a processor,performs an operation for processing a digital image of a document. Theoperation generally includes obtaining a plurality of digital images ofthe document, segmenting at least a first digital image of the pluralityof images into pixels associated with a foreground of the first digitalimage and pixels associated with a background of the first digitalimage, detecting a plurality of contours in the segmented first digitalimage, deciding, for each detected contour of the plurality of contours,whether that contour is an open contour or a closed contour, anddetermining that one or more sides of the document is out-of-boundsbased, at least in part, on the decisions.

Still another embodiment of the present invention includes a processorand a memory storing instructions that, when executed on the processor,performs an operation for processing a digital image. The operationgenerally includes obtaining a plurality of digital images of thedocument, segmenting at least a first digital image of the plurality ofimages into pixels associated with a foreground of the first digitalimage and pixels associated with a background of the first digitalimage, detecting a plurality of contours in the segmented first digitalimage, deciding, for each detected contour of the plurality of contours,whether that contour is an open contour or a closed contour, anddetermining that one or more sides of the document is out-of-boundsbased, at least in part, on the decisions.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example computing environment that may be used topractice techniques presented herein.

FIG. 2 illustrates various components of a long document detectorconfigured to whether a document in a digital image is a long document,according to certain aspects of the present disclosure.

FIG. 3 illustrates various components of an out-of-bound detectionmodule configured to detect whether one or more sides of a document in adigital image is out of bounds of the digital image, according tocertain aspects of the present disclosure

FIG. 4 illustrates an example digital image of a document, according tocertain aspects of the present disclosure.

FIG. 5 illustrates a method for processing an image of a document,according to certain aspects of the present disclosure.

FIG. 6 illustrates a method for determining whether a document is out ofbounds of a digital image, according to certain aspects of the presentdisclosure.

FIG. 7 illustrates example contours found in a digital image, accordingto certain aspects of the present disclosure.

FIG. 8 illustrates drawing bounding rectangles around contours found ina digital image, according to certain aspects of the present disclosure.

FIG. 9 illustrates an example image processing system that identifiesduplicate copies of a form in a document, according to certain aspectsof the present disclosure.

DETAILED DESCRIPTION

Optical character recognition (OCR) techniques are generally used toconvert images of text into computer-encoded text. OCR results tend tobe more accurate when used to evaluate high-resolution, low-noise imagesof typed, black text against a white background. However, in practice,text in digital images is often noisy, obscured, or otherwise less thanideal. In some cases, for example, a physical document may be relativelyobscured or deteriorated as a result of decomposition, excessive use,folding, fingerprints, water damage, or mildew at the time an image ofthe document is captured. Of course, an image of a document may be ofpoor-quality for a variety of other reasons (e.g., if the document is nolonger extant and better images therefore cannot be obtained). Poorimage quality tends to increase OCR processing time and decrease finalaccuracy. Thus, OCR techniques often fail to produce satisfactoryresults on poor-quality images.

In order to make information more readily accessible and searchable,individuals, businesses, and governmental agencies often digitize paperforms. For example, the Internal Revenue Service (IRS) may wish todigitize tax forms (e.g., 1040, W2, 1098-T, or 1099-MISC) submitted onpaper so that information from the tax forms can be inspected for errorsby an automated process. In another example, a law firm may digitize alarge number of paper forms received in response to a discovery requestso that the documents can be electronically searched for certainkeywords. In another example, a web-based genealogical research companymay wish to digitize a large number of death certificates in order tomake information from the death certificates electronically searchablefor customers. In yet another example, a consumer may wish to digitize alarge number of receipts to keep track of how much that consumer isspending.

In some cases, individuals may wish to use commercially availablesoftware, capable of performing OCR on images of digitized documents(e.g., receipts, pay stubs, etc.), to keep track of their financials.For example, in some cases, a user of the software may capture an imageof a receipt related to a financial transaction for use within thesoftware. However, in some cases, due to the limitations of cameras,such as restricted fields of view, a document may be too long to capturein a single image of sufficient quality to identify the document'stextual content. Further, without a reference point, it may be difficultfor a computer executing the software to determine whether an object,such as a document, is long/tall in an image. For example, it ispossible to capture the world's largest building in a single image, andit is also possible to capture a single image of a toy house. However,the computer may not readily be able to determine which of the world'slargest building or the toy house is actually taller in real life.

Accordingly, aspects of the present disclosure generally proposetechniques for solving the above-identified problem related to notknowing a height difference between two objects without a referencepoint, and specifically for detecting whether a document captured in animage is a long document. Doing so may improve OCR performance, reduceprocessing time, and prevent certain errors that occur when anindividual is trying to digitize a long document. For example,techniques presented herein may improve the functioning of a computer byreducing power consumption and processing resources (e.g., time using aprocessor) waste at the computer by allowing the computer to stopprocessing the image of the document when it is determined that thedocument is a long document or when the document is determined to be outof bounds of the image. In other words, the computer does not have towaste power and processing resources on processing an image of adocument that is of poor quality (e.g., the document is out of bounds ofthe image) or an image of a document that is incomplete.

As discussed below, identifying whether a document in an image is a longdocument may be based on various factors. For example, identifyingwhether a document is a long document may be based on factors such asthe size of font detected on the document, bounding informationassociated with edges of the document, and/or dimensions/aspect ratio ofthe document appearing in the image, as described in greater detailbelow.

In some cases, when the detects a long document in a user-capturedimage, the software may stop processing the user-captured image andalert the user that the document is too long to be captured in a singleimage. The software may then direct the user to capture multiple imagesof the document that cover different portions of the document. Thesoftware may then process the multiple images (e.g., by stitching themultiple images together) and perform OCR on the document.

FIG. 1 illustrates a computing environment 100 that may be used toperform techniques described in the present disclosure. A computingdevice 110 and a server 104 communicate via a network 102. As shown, thecomputing device 110 includes a camera 112 used to capture images ofdocuments. In addition, the computing device 110 may be used to executeapplications 114 (e.g., financial management software). In some cases, auser of the computing device 110 captures a digital image 116 (e.g., asillustrated in FIG. 4) of a document using the camera 112. One of theapplications 114 may send the digital image 116 of the document to theserver 104. In an alternative embodiment, a scanner may be used in placeof the camera 112.

As noted above, in some cases, the document captured in the digitalimage 116 may be too long to capture in a single image of sufficientquality to identify the document's textual content. According to certainaspects, in such a case, a long document detector 118 in the computingdevice 110 may determine that the document in the digital image 116 is along document. The long document detector 118 may make thisdetermination based on various factors such as the size of font detectedon the document, bounding information associated with edges of thedocument, and/or dimensions/aspect ratio of the document appearing inthe image, as described in greater detail below. Further, while only asingle digital image 116 is illustrated in FIG. 1, it should beunderstood that the long document detector 118 may make thedetermination of whether a document is a long document over a pluralityof digital images (e.g., a live video stream).

According to aspects, the computing device 110 may also include anout-of-bounds detector 120 that is configured to determine boundinginformation associated with the document in the digital image 116. Forexample, the out-of-bounds detector 120 may be configured to determinewhether one or more portions (e.g., edges) of the document in thedigital image 116 are out of bounds of the digital image 116 (e.g., theone or more portions of the document are not contained within thedigital image 116). As noted, this bounding information may be used bythe long document detector 118 in determining whether the document inthe digital image 116 is a long document. According to certain aspects,while the out-of-bounds detector 120 is illustrated as a separatecomponent from the long document detector 118, it should be understoodthat the out-of-bounds detector 120 and the long document detector 118may comprise a single component.

The computing device 110 also includes a user alert module 122 that maydirect a user to capture multiple digital images of the document,focused on different portions of the document, for example, if it isdetermined that the document in the digital image 116 is a longdocument. In some cases, if the document is determined to be a longdocument, the user alert module 122 may direct the user of the computingdevice 110 to scan the document at close range using the camera 112 in avideo capture mode. Accordingly, once the user of the computing device110 has captured images of the long document with sufficient quality(e.g., textual content on the document is discernable), the computingdevice may perform optical character recognition (OCR) on the documentto determine the documents textual content and store a digitalizedversion of the document in a searchable database.

Additionally, in some cases, the server 104 may include a long documentdetector 124 and an out-of-bounds detector 126 that can perform the samefunctions as the long document detector 118 and out-of-bounds detector120. For example, instead of the long document detector 118 determiningwhether a document in the digital image 116 is a long document, one ormore applications 114 in the computing device 110 may transmit thedigital image 116 to the server 104 (e.g., via the Network 102) and thelong document detector 124 may determine that the document in thedigital image 116 is a long document. According to aspects of thepresent disclosure, if the long document detector 124 determines thedocument to be a long document, the server 104 may direct the user alertmodule 122 in the computing device 110 to alert the user to capturemultiple, focused images of the document, as described above.

While the server 104 is depicted as a single server, it should beunderstood that techniques of the present disclosure can be applied in acloud-based scheme using multiple physical or virtual computingresources. The long document detector 124 and the out-of-bounds detector126 may be distributed across different computing resources as part of acloud-based computing system. Further, the computing device 110 may beconsidered to be representative of a variety of devices, such as amobile device, a cellular phone, a smart phone, a tablet, a laptopcomputer, a desktop computer, a personal digital assistant (PDA), or anycomputing system that may execute software applications.

FIG. 2 illustrates a more detailed view of various components that makeup the long document detector 118. As illustrated, the long documentdetector 118 may comprise a document type classification module 202which determines the type of a document (e.g., a grocery receipt, a taxform, a pay stub, and invoice, etc.) in a user-captured digital image(e.g., digital image 116). For example, a user of the computing device110 may capture a digital image 116 (or multiple images, e.g., a videostream) of the form. An example digital image 116 is illustrated in FIG.4. The digital image 116 may then be sent to and received at thedocument type classification module 202, which may process the digitalimage 116 to determine the type of document contained within the digitalimage 116. For example, the document type classification module 202 mayperform optical character recognition (OCR) on the digital image 116 todetermine the textual content of the document.

The document type classification module 202 may then compare the textualcontent of the document with a pre-defined dictionary of words.According to aspects, the pre-defined dictionary comprises a list ofwords that are indicative of certain types of documents. For example, insome cases, the pre-defined dictionary may comprise a list of wordsassociated with receipts, such as store names (e.g., Target®, Best Buy®,Costco®, etc.) or names of items typically sold at these stores. Thepre-defined dictionary may also comprise words associated with taxforms, such as “W2”, “1099”, “Tax return”, “IRS”, etc. Thus, forexample, the document type classification module 202 may recognize thatthe document in the digital image 116 comprises the word “Costco” anddetermines that the type of the document in the digital image 116 is areceipt, for example, as illustrated in FIG. 4.

The document type classification module 202 may then load pre-definedmetrics (e.g., from memory in the computing device 110, not shown)associated with the detected type of document. For example, assumingthat the type of document is a receipt, the associated metrics mayinclude a font size range (e.g., between 24 and 36 pixels), an aspectratio threshold (e.g., 3-to-1), and a minimum height percentage of thedocument as compared to the whole digital image 116 (e.g., 80%).According to aspects, these pre-defined metrics may be used to determinewhether or not the document in the digital image 116 is a long document,for example, as described in greater detail below.

In some cases, if the document type classification module 202 is unableto determine the type of the document in the digital image 116, thedocument type classification module 202 may load default pre-definedmetrics or may provide an indication to the user of the computing device110 (e.g., via the user alert module 122) to take a more close-uppicture of the document (e.g., if the document type classificationmodule 202 is unable to recognize the textual content on the document,for example, due to the text size being recognized as too small).

According to aspects, once the type of the document in the digital image116 is determined and the pre-defined metrics are loaded, the digitalimage 116 may be sent to and received at a document characteristicsdetection module 206. According to aspects, the document characteristicsdetection module 206 analyzes the digital image 116 and determinesadditional characteristics associated with the document in the digitalimage 116, for example, based on the analysis. For example, asillustrated, the document characteristics detection module 206 mayanalyze the digital image 116 with a font size detection module 208 todetermine a text size of text appearing on the document. The documentcharacteristics detection module 206 may also analyze the digital image116 to determine bounding information associated with edges of thedocument in the digital image 116 using an out-of-bounds detectionmodule 210. Further, the document characteristics detection module 206may also analyze the digital image 116 to determine the dimensions andaspect ratio of the document in the digital image 116, for example,using a document dimension and aspect ratio detection module 212.According to aspects, these analyses may then be used to determinewhether the document in the digital image 116 is a long document, asdescribed in greater detail below.

As noted, the document characteristics detection module 206 includes afont size detection module 208 for determining the font size of the texton the document in the digital image 116. According to aspects, the fontsize detection module 208 may determine the font size of the text based,for example, on an analysis of text blocks. For example, the font sizedetection module 208 may determine a number of text blocks on thedocument in the digital image 116 by drawing bounding rectangles aroundlines of text on the document. For example, given a document with threelines of text, the font size detection module 208 determines that thereare three text blocks by drawing bounding rectangles around each line oftext. The font size detection module 208 may then determine the averageheight of the text blocks. According to aspects, based on the averageheight of the text blocks, the font size detection module 208 mayapproximate the size of the text on the document in the digital image116. That is, the average height of the text blocks may be an estimatorof text size.

Additionally, as noted above, the document characteristics detectionmodule 206 includes an out-of-bounds detection module 210 fordetermining bounding information associated with edges of the documentin the digital image 116. For example, the out-of-bounds detectionmodule 210 may be used for determining whether any part of the documentin the digital image 116 is out of bounds of the digital image 116.Further, as will be described in greater detail below, the boundinginformation may be used when determining whether the document in thedigital image 116 is a long document.

FIG. 3 illustrates a more detailed view of the out-of-bounds detectionmodule 210. As illustrated, the out-of-bounds detection module 210comprises an image processing module 302 that prepares the digital image116 for contour analysis by the contour analyzer 304. Based on thecontour analysis, described in greater detail below, an out-of-boundsdecision module 306 may determine that one or more edges of the documentin the digital image 116 are out of bounds of the digital image 116.

As noted, the image processing module 302 processes the digital image116 to prepare the digital image 116 for contour analysis. For example,the image processing module 302 may receive the digital image 116. Theimage processing module 302 may then scale the digital image 116 down(e.g., reduce the size of the digital image 116). The scaled-downdigital image 116 may then be processed by a clustering algorithm (e.g.,OpenCV kmeans method) to separate the pixels in the scaled-down digitalimage 116 into two clusters: one cluster representing the foreground(e.g., the document in the digital image 116), and another clusterrepresenting the background. According to aspects, the image processingmodule 302 may then create a segmented image such that pixels belongingto the foreground cluster of the scaled-down digital image 116 are blackand pixels belonging to the background cluster of the scaled-downdigital image 116 are white. According to aspects, creating thissegmented image allows the contour analyzer 304 to more-easilydetermine/find the contours in the segmented image (i.e., correspondingto the digital image 116).

Accordingly, once the image processing module 302 has created thesegmented image, the contour analyzer 304 may process the segmentedimage to determine contours in the segmented image. The contour analyzer304 may then analyze a hierarchy of the contours (e.g., the nesting ofcontours) found in the segmented image and determine which contours areopen or closed. A simple example of a closed contour is a rectanglewhose four sides are all contained within the segmented image, whereasan open contour is, for example, a rectangle with one or more sides ofthe rectangle outside of the segmented image. More specifically, an opencontour is one that does not have any child contour in the hierarchy(e.g., the contour does not bound another contour). According toaspects, for each continuous contour (e.g., whether open or closed)found in the segmented image, the contour analyzer 304 is configured tocreate a bounding rectangle that encompasses that contour. The contouranalyzer 304 may then compute the area (e.g., pixels squared) of eachbounding rectangle and determine the bounding rectangle with the largestarea.

According to aspects, if the bounding rectangle with the largest areabounds an open contour and if one or more sides of this boundingrectangle touches one or more of the edges of the segmented image, theout-of-bounds decision module 306 may conclude that part of the documentin the digital image 116 is out of bounds. Otherwise, if the boundingrectangle with the largest area bounds an open contour and if none ofthe sides of this bounding rectangle touches the edges of the segmentedimage, the out-of-bounds decision module 306 may conclude that thedocument in the digital image 116 is not out of bounds of the digitalimage 116. Likewise, if the bounding rectangle with the largest areabounds a closed contour, the out-of-bounds decision module 306 mayconclude that the document in the digital image 116 is not out of boundsof the digital image 116.

According to aspects, if the out-of-bounds decision module 306determines that part of the document is out of bounds of the digitalimage 116, out-of-bounds decision module 306 may direct the user alertmodule 122 to inform the user of the computing device 110 that thedocument in the digital image 116 is out of bounds and direct the userto capture an additional digital image fully encompassing the document.

Additionally, in some cases, if the out-of-bounds decision module 306determines that part of the document is out of bounds of the digitalimage 116 (e.g., the bounding rectangle with the largest area bounds anopen contour and one or more sides of this bounding rectangle touchesone or more edges of the digital image 116), the contour analyzer 304may determine the sides of the document that are out of bounds of thedigital image 116. According to aspects, the contour analyzer 304 maydetermine which sides of the document are out of bounds of the digitalimage 116 based on an analysis of the open contour that is bounded bythe bounding rectangle with the largest area. For example, the contouranalyzer 304 may determine which side of the bounding rectangle with thelargest area touches an edge of the digital image 116, and may deducewhich corners of this bounding rectangle are out of bounds. For example,if the top side of this bounding rectangle touches an edge of thedigital image 116, the contour analyzer 304 may determine that the topleft and top right corners are missing. According to aspects, based onthe missing corners, the contour analyzer 304 may deduce which sides ofthe document are out of bounds of the digital image 116.

Further, once the contour analyzer 304 determines the sides of thedocument that are out of bounds of the digital image 116, the contouranalyzer 304 may supply the user alert module 122 with this information,which may, in turn, inform the user of the computing device 110 of thesides of the document that are out of bounds. This information may beused by the user of the computing device to capture an additional imageof the document, for example, as described above. According to aspects,this process of determining the bounding information of the document inthe digital image 116 will be described in greater detail below, withreference to FIGS. 6-8.

Returning to FIG. 2, as noted above, the document characteristicsdetection module 206 includes a document dimension and aspect ratiodetection module 212 for determining the dimensions and aspect ratio ofthe document in the digital image 116. According to aspects, thedocument dimension and aspect ratio detection module 212 may beconfigured to determine the dimensions and aspect ratio of the documentin the digital image 116 by again analyzing the bounding rectangle withthe largest area (e.g., as described above in relation to theout-of-bounds detection module 210). In some cases, the documentdimension and aspect ratio detection module 212 may independentlydetermine the contours in the digital image 116 (e.g., using the same orsimilar techniques described above) or may re-use contour informationdetermined by the contour analyzer 304. According to aspects, thedocument dimension and aspect ratio detection module 212 may determinethe height and width of the bounding rectangle with the largest area(e.g., the rectangle that bounds the document in the digital image 116)and, using the determined height and width, determine the aspect ratio(e.g., width divided by height).

According to aspects, the long document detector 118 may determine thecharacteristics of the document described above (e.g., font size,bounding information, and dimensions/aspect ratio) over multipleconsecutive digital images and may keep track of the results. Theseresults (i.e., the document characteristics), in addition to thepre-defined metrics corresponding to the determined document typedescribed above and one or more conditions (e.g., decision rules), maythen be used by the long document decision module 214 to determinewhether the document in the digital image 116 is a long document or not,as described below.

For example, in some cases, the long document decision module 214 maydetermine the document in the digital image 116 to be a long document ifthe font size of the text on the document (e.g., as determined by thefont size detection module 208) is less than a lower bound of the loadedpre-defined font size range over the multiple consecutive images and ifthe bounding information of the document in the multiple consecutiveimages indicates that one or more sides of the document areout-of-bounds. In such case, it may be assumed that the camera 112 isbeing held far away from the document such that the text on the documentis small and also that the user of the computing device 110 hasdifficulty fitting the document within a single camera frame.

In other cases, the long document decision module 214 may determine thedocument in the digital image 116 to be a long document if, over themultiple consecutive digital images, a height dimension of the document(e.g., as determined by the document dimension and aspect ratiodetection module 212) is greater than the pre-defined minimum heightpercentage threshold of each of the multiple consecutive digital images(e.g., the document's height is greater than ‘X’ percent of the digitalimage 116), and if the aspect ratio of the document (e.g., as determinedby the document dimension and aspect ratio detection module 212) isgreater than the pre-defined aspect ratio threshold for the multipleconsecutive images. In such a case, it may be assumed that the user ofthe computing device 110 held the camera 112 high enough so that thewhole document would fit inside one camera frame, and since the camerawas held high, the text on the document is small. Additionally, bychecking whether the document height is greater than the defined minimumheight percentage threshold of a digital image, this reduces thepossibility of a misdetermination where the document is actually a shortdocument but the user just happened to hold the camera at a height suchthat the text size appears small.

In yet other cases, the long document decision module 214 may determinethe document in the digital image 116 to be a long document if the fontsize of the text on the document in the digital image 116 is within thepre-defined font size range and if the bounding information indicatesthat one or more sides of the document is consistently out of boundsover the multiple consecutive images.

Otherwise, if the font size of the text on the document in the digitalimage 116 is at the top of the font size range or above and if thedocument is completely inside the first digital image 116 (e.g., thebounding information indicates that no sides of the document are out ofbounds of the digital image 116), then the long document decision module214 may conclude that the document in the digital image 116 is not along document.

According to aspects, if the long document decision module 214determines that the document in the digital image 116 is a long document(e.g., based on the techniques described above), the long documentdecision module 214 may direct the user alert module 122 to alert theuser that the document is too long to be captured in a single image anddirect the user to capture multiple digital images of the document, eachdigital image of the multiple digital images focusing on a differentportion of the document such that a combination of each digital image ofthe multiple digital images of the document captures the entiredocument. For example, based on an identification that a first image ofa document includes missing edges at the top and bottom of the document,computing device 110 can request that the user capture additional imagesrepresenting a portion of the document above the portion captured in thefirst image and a portion of the document below the portion captured inthe first image. The computing device 110 may then stitch together themultiple digital images to generate a single digital image of thedocument and may feed the single digital image of the document into anOCR module for text extraction and analysis.

FIG. 5 is a flow diagram illustrating an exemplary method 500 forprocessing digital images of a document, for example, to determine ifthe document is a long document, according to certain aspects of thepresent disclosure. The method 500 may be performed, for example, by acomputing device 110 and/or a server 104.

The method 500 begins at 502 by obtaining a plurality of digital imagesof the document. As noted above, this may involve a user capturing theplurality of images (e.g., digital image 116) of the document using acamera (e.g., camera 112). The plurality of digital images may then beforwarded to and obtained by, for example, a component configured todetect whether the document is a long document (e.g., long documentdetector 118). According to aspects, a long document may be a documentthat is too long to capture in a single image of sufficient quality toidentify the document's textual content.

At 504, a type of the document in the plurality of images is determined.For example, the document type classification module 202 may receive theplurality of digital images of the document and perform opticalcharacter recognition (OCR) on the plurality of digital images todetermine the textual content of the document. The document typeclassification module 202 may then compare the textual content of thedocument with a dictionary of words that are indicative of certain typesof documents. For example, in some cases, the document may include astore name such as “Groceries-R-Us”. The document type classificationmodule 202 may search the dictionary of words for “Groceries-R-Us”, andmay determine that “Groceries-R-Us” is listed in the dictionary of wordsas being associated with the document type: receipts. Thus, the documenttype classification module may determine the type of the document in theplurality of documents to be a receipt.

At 506, the computing device 110 loads one or more pre-defined metricsassociated with documents of the determined type. As noted above,pre-defined metrics may include a font size range for text appearing ondocuments of the determined type, an aspect ratio threshold, and/or aminimum height percentage of documents of the determined type ascompared to a whole digital image of that type of document. For example,assuming that the document captured in the plurality of images is areceipt, the pre-defined metrics may include a font range between 24 and36 pixels, an aspect ratio threshold of 3-to-1, and a minimum heightpercentage of the document as compared to the whole digital image 116 of80%. According to aspects, these pre-defined metrics represent averagevalues for these three categories of metrics based on typical, non-longreceipts. That is, a typical, non-long receipt generally has a font sizeof 24 to 36 pixels, an aspect ratio of 3-to-1, and usually takes up 80%of the height of an image capturing this non-long receipt.

At 508, the computing device 110 determines one or more characteristicsof the document in the plurality of digital images, based on one or moreanalyses performed on the plurality of digital images of the document.

For example, the computing device 110 may determine a font size oftextual content on the document, bounding information associated withone or more sides of the document, dimensions of the document, and/or anaspect ratio of the document. As noted above, the computing device 110may determine the font size of the textual content on the document basedon an average text block size, for example, as described above.

Additionally, the computing device 110 may determine the boundinginformation by, for at least one image of the plurality of digitalimages, scaling down the plurality of images of the document, segmentingthe scaled down image into groups of pixels corresponding to theforeground of the scaled down image (e.g., representing the document)and another group of pixels corresponding to the background of theimage. The computing device may then find the contours in the segmentedimage and draw bounding rectangles around the found contours. Accordingto aspects, the computing device 110 may then deduce that at least oneside of the document is out of bounds of the segmented image if therectangle with the largest area in the segmented image bounds an opencontour and if one or more sides of this rectangle touch an edge of thesegmented image. Additionally, the computing device may determine thedimensions and aspect ratio of the document by measuring the height andwidth of the rectangle with the largest area, as described above.

At 510, the computing device 110 compares the one or morecharacteristics of the document with the one or more pre-definedmetrics. For example, the computing device 110 may compare thedetermined font size of the textual content on the document with thefont size range. According to aspects, the computing device 110 maycompare each of the determined characteristics of the document withtheir corresponding pre-defined metrics.

At 512, the computing device 110 determines the document to be a longdocument based, at least in part, on the comparison. For example, thecomputing device 110 may determine that the document in the plurality ofimages is a long document when the font size of the textual content onthe document is less than a lower bound of the loaded pre-defined fontsize range over multiple consecutive images of the plurality of images(e.g., a configurable number of images) and when the boundinginformation of the document in the multiple consecutive images indicatesthat one or more sides of the document are out-of-bounds. Additionally,the computing device 110 may determine the document in the plurality ofimages to be a long document when, over the multiple consecutive digitalimages, a height dimension of the document is greater than thepre-defined minimum height percentage threshold of each of the multipleconsecutive digital images and when the aspect ratio of the document isgreater than the pre-defined aspect ratio threshold for the multipleconsecutive images. Further, the computing device 110 may determine thatthe document in the plurality of images is a long document when the fontsize of the text on the document in the plurality of images is withinthe pre-defined font size range and when the bounding informationindicates that one or more sides of the document is consistently out ofbounds over the multiple consecutive images.

At 514, if the document in the plurality of documents is not a longdocument, the method 500 ends. In some cases, the computing device 110may determine that the document in the plurality of images is not a longdocument if the font size of the text on the document in the digitalimage 116 is at or above the top of the font size range and if thedocument is completely inside the first digital image 116 (e.g., thebounding information indicates that no sides of the document are out ofbounds of the digital image 116). In such a case, the user of thecomputing device 110 may be allowed to proceed and store a digital copyof the document.

If, however, at 514, the document in the plurality of documents is along document, the method continues to 516 where the computing device110 directs the user to capture multiple images of the document, eachcovering a different portion of the document. In some cases, thecomputing device 110 may direct the user to scan the document with thecamera 112 (e.g., using a video capture mode). In response, thecomputing device may stitch together these images and store a digitalcopy of the document. In some cases, the computing device may performOCR on the document and store textual content associated with thedocument in a searchable database.

FIG. 6 illustrates example operations 600 for determining boundinginformation of a document in a digital image, according to certainaspects of the present disclosure. According to certain aspects, exampleoperations 600 may be performed by one or more components capable ofprocessing a digital image, such as the out-of-bounds detection module210 of the computing device 110.

Operations 600 begin at 602 with the out-of-bounds detection module 210obtaining a first digital image of a document. An example of a firstdigital image (e.g., digital image 116) is illustrated in FIG. 7.Additionally, while not illustrated in FIG. 6, the out-of-boundsdetection module 210 may scale down the first digital image, forexample, to make it easier and faster to determine the contours of thefirst digital image.

According to aspects, at 604, the out-of-bounds detection module 210segments the first digital image into two groups of pixels: pixelsassociated with a foreground of the first digital image (e.g., coloredwhite, not shown) and pixels associated with a background of the firstdigital image (e.g., colored black, not shown). In some cases, theout-of-bounds detection module 210 segments the first digital image byprocessing the first digital image with a clustering algorithm (e.g.,OpenCV kmeans algorithm). According to aspects, creating this segmentedimage allows the out-of-bounds detection module 210 to more-easilydetermine/find the contours in the segmented image (i.e., correspondingto the digital image 116).

According to aspects, once out-of-bounds detection module 210 hascreated the segmented first digital image, at 606, the out-of-boundsdetection module 210 detects contours in the segmented first digitalimage.

At 608, the out-of-bounds detection module 210 analyzes a hierarchy ofthe contours (e.g., the nesting of contours) found in the segmentedfirst digital image and decides which contours are open or closed.According to aspects, and as noted above an example of a closed contouris a rectangle whose four sides are all contained within the segmentedimage. An example closed contour that may be found when processing thesegmented first digital image is illustrated at 702A in FIG. 7.According to aspects, an open contour is, for example, a rectangle withone or more sides of the rectangle outside of the segmented image. Morespecifically, an open contour is one that does not have any childcontour in the hierarchy (e.g., the contour does not bound anothercontour). FIG. 7 illustrates an example of an open contour that may befound when processing the segmented first digital image, which maycomprise lines 702B, 702C, and 702D.

At 610, for each continuous contour (e.g., whether open or closed) foundin the segmented image, the out-of-bounds detection module 210 isconfigured to create a bounding rectangle that encompasses that contour.For example, as illustrated in FIG. 8, the out-of-bounds detectionmodule 210 may create a first bounding rectangle 802A around the closedcontour 702A and a second bounding rectangle 802B around the opencontour corresponding to lines 702B, 702C, and 702D.

At 612, the out-of-bounds detection module 210 determines the area(e.g., pixels squared) of each bounding rectangle. For example, withreference to FIG. 8, the out-of-bounds detection module 210 determinesthe area of the first bounding rectangle 802A and the area of the secondbounding rectangle 802B. The out-of-bounds detection module 210 may thendetermine which of the first bounding rectangle 802A or the secondbounding rectangle comprises the most area. In the example illustratedin FIG. 8, the out-of-bounds detection module 210 may determine that thesecond bounding rectangle 802B (e.g., which bounds the open contourcomprising lines 702B-702D) comprises the most area.

At 614, the out-of-bounds detection module 210 determines whether one ormore sides of the document in the segmented first digital image are outof bounds. For example, if the bounding rectangle with the largest areain the first digital image bounds an open contour and if one or moresides of this bounding rectangle touches one or more of the edges of thesegmented image, the out-of-bounds detection module 210 may concludethat part of the document in the first digital image is out of bounds.For example, with reference to FIG. 8, since the second boundingrectangle 802B bounds an open contour (e.g., lines 702B-702D) and sincethe second bounding rectangle 802B touches one of the sides of the firstdigital image (e.g., at 804), the out-of-bounds detection module 210determines that a portion of the document in the first digital image isout of bounds.

Otherwise, as noted above, if the bounding rectangle with the largestarea bounds an open contour and if none of the sides of this boundingrectangle touches the edges of the segmented image, the out-of-boundsdetection module 210 may conclude that the document in the first digitalimage is not out of bounds of the first digital image. Likewise, if thebounding rectangle with the largest area bounds a closed contour, theout-of-bounds detection module 210 may conclude that the document in thefirst digital image is not out of bounds of the first digital image.

According to aspects, if the out-of-bounds detection module 210determines that one or more sides of the document are out of bounds ofthe first digital image, at 614, the out-of-bounds detection module 210also determines which sides of the document are out of bounds. Forexample, the out-of-bounds detection module 210 may analyze the secondbounding rectangle 802B and determine which corners of the secondbounding rectangle 802B are captured in the first digital image, as wellas which corners of the bounding rectangle 802 are out of bounds of thefirst digital image. For example, with reference to FIG. 8, theout-of-bounds detection module 210 may determine that the bottom leftand bottom right corners are contained within the first digital imageand that the top left and top right corners are out of bounds of thefirst digital image. According to aspects, based on the top left and topright corners being out of bounds, the out-of-bounds detection module210 may deduce that the top side of the document in the first digitalimage is out of bounds. In some cases, the out-of-bounds detectionmodule 210 may determine which corners are out of bounds (and deducewhich side of the document is out of bounds) based on an analysis ofwhich side of the second bounding rectangle 802B touches an edge of thefirst digital image. For example, as illustrated in FIG. 8, theout-of-bounds detection module 210 will determine that the top-side ofthe second bounding rectangle 802B touches the edge of the first digitalimage at 804, and deduce that the top side of the document is out ofbounds.

According to aspects and as noted above, if the out-of-bounds detectionmodule 210 determines that one or more sides of the document in thefirst digital image are out of bounds, the out-of-bounds detectionmodule 210 may direct the user alert module 122 to notify the user ofthe computing device 110 to capture the at least one additional digitalimage of the document.

According to aspects, when a font size on the document (e.g., asdetermined by the font size detection module 208) is greater than orequal to an upper font size threshold, the user alert module 210 alertsthe user to capture an image of the entire document at a furtherdistance than the first digital image. In such a case, the computingdevice 110 may conclude that, since the font size of the document isgreater than or equal to an upper font size threshold/bound, allowingthe user to take an image of the document at a further distance willstill result in a digital image where the textual content of thedocument is still discernable.

However, when the font size of text on the document (e.g., as determinedby the font size detection module 208) is less than or equal to a lowerfont size threshold/bound, the user alert module 210 alerts the user tocapture multiple images of the document, each image of the multipleimages focusing on a different portion of the document such that acombination of each image of the multiple images of the documentcaptures the entire document. Thereafter, the computing device 110 maystitch together the multiple images to generate a single image of thedocument.

FIG. 9 illustrates an example image processing system 900 thatdetermines, among other things, whether a document in a digital image isa long document, according to certain aspects of the present disclosure.As shown, the image processing system 900 includes, without limitation,a central processing unit (CPU) 902, one or more I/O device interfaces904 which may allow for the connection of various I/O devices 914 (e.g.,keyboards, displays, mouse devices, pen input, etc.) and camera 916 tothe image processing system 900, network interface 906, a memory 908,storage 910, and an interconnect 912.

CPU 902 may retrieve and execute programming instructions stored in thememory 908. Similarly, the CPU 902 may retrieve and store applicationdata residing in the memory 908. The interconnect 912 transmitsprogramming instructions and application data, among the CPU 902, I/Odevice interface 904, network interface 906, memory 908, and storage910. CPU 902 can represent a single CPU, multiple CPUs, a single CPUhaving multiple processing cores, and the like. Additionally, the memory908 represents random access memory. Furthermore, the storage 910 may bea disk drive. Although shown as a single unit, the storage 910 may be acombination of fixed or removable storage devices, such as fixed discdrives, removable memory cards or optical storage, network attachedstorage (NAS), or a storage area-network (SAN).

As shown, memory 908 includes a long document detector 118 and a useralert module 122. The long document detector 118 comprises a documenttype classification module 202, a document characteristics detectionmodule 206, and a long document decision module 214. A digital image ofa document can be sent to the long document detector 118 from the I/Odevices 914, camera 916, or from another source, such as the network102. The document type classification module 202 can determine a type ofthe document in the digital image and load pre-defined metrics (e.g.,pre-defined metrics 918) associated with documents of the determinedtype. The document characteristics detection module 206 can determineone or more characteristics associated with the documents, such as afont size of textual content on the document, bounding informationassociated with one or more sides of the document, dimensions of thedocument, and/or an aspect ratio of the document. The long documentdecision module 214 can decide whether the document in the digital imageis a long document, for example, based on the loaded pre-definedmetrics, the one or more characteristics of the document, and one ormore conditions (e.g., decision rules).

As shown, storage 910 includes the digital image 116 (e.g., captured bythe user via the camera 916), pre-defined metrics 902, and decisionrules 216. According to aspects, the pre-defined metrics 902 may includea font size range for text appearing on documents of the determinedtype, an aspect ratio threshold, and/or a minimum height percentage ofdocuments of the determined type as compared to a whole digital image ofthat type of document. Additionally, according to certain aspects, thedecision rules 216 may be used by the long document detector 118 todecide whether the document in the digital image 116 is a long document,for example, as explained above.

According to certain aspects, if it is determined that the document inthe digital image 116 is a long document, the long document decisioncomponent may generate a notification and provide it to the user alertmodule 122. Upon receiving the notification, the user alert module 122may inform the user of the computing device 110 to capture additionaldigital images of the document, each focusing on a different portion ofthe document. According to aspects, the computing device 110 may stitchtogether the additional digital images, perform OCR on the stitchedtogether digital images, and store textual content of the document,recognized as a result of OCR, as a searchable digital document 920. Insome cases, the computing device 110 may transmit the digital image 116and/or the searchable digital document 920 via the network interface 906for storage in a cloud database.

Note, descriptions of embodiments of the present disclosure arepresented above for purposes of illustration, but embodiments of thepresent disclosure are not intended to be limited to any of thedisclosed embodiments. Many modifications and variations will beapparent to those of ordinary skill in the art without departing fromthe scope and spirit of the described embodiments. The terminology usedherein was chosen to best explain the principles of the embodiments, thepractical application or technical improvement over technologies foundin the marketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

In the preceding, reference is made to embodiments presented in thisdisclosure. However, the scope of the present disclosure is not limitedto specific described embodiments. Instead, any combination of thefollowing features and elements, whether related to differentembodiments or not, is contemplated to implement and practicecontemplated embodiments. Furthermore, although embodiments disclosedherein may achieve advantages over other possible solutions or over theprior art, whether or not a particular advantage is achieved by a givenembodiment is not limiting of the scope of the present disclosure. Thus,the following aspects, features, embodiments and advantages are merelyillustrative and are not considered elements or limitations of theappended claims except where explicitly recited in a claim(s). Likewise,reference to “the invention” shall not be construed as a generalizationof any inventive subject matter disclosed herein and shall not beconsidered to be an element or limitation of the appended claims exceptwhere explicitly recited in a claim(s).

Aspects of the present disclosure may take the form of an entirelyhardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.” Furthermore,aspects of the present disclosure may take the form of a computerprogram product embodied in one or more computer readable medium(s)having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples a computer readable storage medium include: anelectrical connection having one or more wires, a hard disk, a randomaccess memory (RAM), a read-only memory (ROM), an erasable programmableread-only memory (EPROM or Flash memory), an optical fiber, a portablecompact disc read-only memory (CD-ROM), an optical storage device, amagnetic storage device, or any suitable combination of the foregoing.In the current context, a computer readable storage medium may be anytangible medium that can contain, or store a program.

While the foregoing is directed to embodiments of the presentdisclosure, other and further embodiments of the disclosure may bedevised without departing from the basic scope thereof, and the scopethereof is determined by the claims that follow.

What is claimed is:
 1. A computer-implemented method for processingdigital images of a document, comprising: obtaining a first digitalimage of a document from a user; determining a document type of thedocument in the first digital image based on a textual content of thedocument; determining a font size of text in the document in the firstdigital image; determining that at least one part of the document in thefirst digital image is out of bounds of the first digital image basedon: a bounding rectangle with a largest area corresponding to an opencontour; and the bounding rectangle with the largest area touching oneor more edges of the first digital image; comparing the font size oftext in the document with a font size range of the determined documenttype; determining, based on the comparison and the determination thatthe at least one part of the document is out of bounds of the firstdigital image, that the document in the first digital image is a longdocument; generating, based on the determination that the document inthe first digital image is a long document, an alert for the user tocapture a set of digital images of the document, wherein each digitalimage of the set of digital images of the document is of a differentportion of the document; obtaining the set of digital images of thedocument from the user; generating a second digital image of thedocument based on the obtained set of digital images; and performing OCRon the second digital image of the document.
 2. The method of claim 1,wherein obtaining the set of digital images comprises obtaining the setof digital images via a live stream captured by a camera.
 3. The methodof claim 1, wherein the comparison of the font size of the document withthe font size range of the determined document type further comprisesdetermining the font size of the text in the document is less than alower bound of the font size range.
 4. The method of claim 1, whereindetermining the at least one part of the document in the first digitalimage is out of bounds of the first digital image further comprises:segmenting the first digital image into: a first set of pixelsassociated with a foreground of the first digital image; and a secondset of pixels associated with a background of the first digital image;detecting a set of contours in the segmented first digital image;determining a set of bounding rectangles based on the set of contours;determining an area of each bounding rectangle in the set of boundingrectangles; determining the bounding rectangle of the set of boundingrectangles with the largest area; and determining, for each contour ofthe set of contours, whether the contour is an open contour or a closedcontour, wherein: an open contour is a bounding rectangle with one ormore sides outside of the segmented first digital image; and a closedcontour is a bounding rectangle with all four sides inside of thesegmented first digital image.
 5. The method of claim 1, wherein thedetermining the document type of the document in the first digital imagebased on a textual content of the document further comprises: performingOCR on the first digital image to determine the textual content; andcomparing the textual content to a set of text indicative of certaintypes of documents.
 6. The method of claim 1, where the determining afont size of text in the document in the first digital image furthercomprises: drawing a bounding rectangle around each line of text in thedocument; determining a number of text blocks based on a number ofbounding rectangles; determining an average height of the number of textblocks; and determining an estimated text size of the text in thedocument based on the average height of the number of text blocks. 7.The method of claim 4, wherein the method further comprises determiningthat the document in the first digital image is not a long documentbased on: a determination the document is not out of bounds, wherein thedetermination the document is not out of bounds further comprises: thebounding rectangle with the largest area corresponding to a closedcontour; and the bounding rectangle with the largest area does not touchan edge of the first digital image; and a determination the font size ofthe text in the document is at a top or above the font size range.
 8. Anapparatus for processing digital images of a document, comprising: aprocessor; and a memory having instructions which, when executed by theprocessor, performs an operation for processing a digital image, theoperation comprising: obtaining a first digital image of a document froma user; determining a document type of the document in the first digitalimage, based on a textual content of the document; determining a fontsize of text in the document in the first digital image; determiningthat at least one part of the document in the first digital image is outof bounds of the first digital image based on: a bounding rectangle witha largest area corresponding to an open contour; and the boundingrectangle with the largest area touching one or more edges of the firstdigital image; comparing the font size of text in the document with afont size range of the determined document type; determining, based onthe comparison and the determination that the at least one part of thedocument is out of bounds of the first digital image, that the documentin the first digital image is a long document; generating, based on thedetermination that the document in the first digital image is a longdocument, an alert for the user to capture a set of digital images ofthe document, wherein each digital image of the set of digital images ofthe document is of a different portion of the document; obtaining theset of digital images of the document from the user; generating a seconddigital image of the document based on the obtained set of digitalimages; and performing OCR on the second digital image of the document.9. The apparatus of claim 8, wherein the comparison of the font size oftext in the document with the font size range of the determined documenttype further comprises determining the font size of the text in thedocument is less than a lower bound of the font size range.
 10. Theapparatus of claim 8, wherein the operation for determining the at leastone part of the document in the first digital image is out of bounds ofthe first digital image further comprises: segmenting the first digitalimage into: a first set of pixels associated with a foreground of thefirst digital image; and a second set of pixels associated with abackground of the first digital image; detecting a set of contours inthe segmented first digital image; determining a set of boundingrectangles based on the set of contours; determining an area of eachbounding rectangle in the set of bounding rectangles; determining thebounding rectangle of the set of bounding rectangles with the largestarea; and determining, for each contour of the set of contours, whetherthe contour is an open contour or a closed contour, wherein: an opencontour is a bounding rectangle with one or more sides outside of thesegmented first digital image; and a closed contour is a boundingrectangle with all four sides inside of the segmented first digitalimage.
 11. The apparatus of claim 8, wherein obtaining the set ofdigital images comprises obtaining the set of digital images via a livestream captured by a camera.
 12. The apparatus of claim 8, wherein thedetermining the document type of the document in the first digital imagebased on a textual content of the document further comprises: performingOCR on the first digital image to determine the textual content; andcomparing the textual content to a set of text indicative of certaintypes of documents.
 13. The apparatus of claim 8, wherein thedetermining a font size of text in the document in the first digitalimage further comprises: drawing a bounding rectangle around each lineof text in the document; determining a number of text blocks based on anumber of bounding rectangles; determining an average height of thenumber of text blocks; and determining an estimated text size of thetext in the document based on the average height of the number of textblocks.
 14. The apparatus of claim 10, wherein the operation furthercomprises determining that the document in the first digital image isnot a long document based on: a determination the document is not out ofbounds, wherein the determination the document is not out of boundsfurther comprises: the bounding rectangle with the largest areacorresponding to a closed contour; and the bounding rectangle with thelargest area does not touch an edge of the first digital image; and adetermination the font size of the text in the document is at a top orabove the font size range.
 15. A non-transitory computer-readable mediumcomprising instructions which, when executed on one or more processors,performs an operation for processing a digital image of a document,comprising: obtaining a first digital image of a document from a user;determining a document type of the document in the first digital imagebased on a textual content of the document; determining a font size oftext in the document in the first digital image; determining that atleast one part of the document in the first digital image is out ofbounds of the first digital image based on: a bounding rectangle with alargest area corresponding to an open contour; and the boundingrectangle with the largest area touching one or more edges of the firstdigital image; comparing the font size of text in the document with afont size range of the determined document type; determining, based onthe comparison and the determination that the at least one part of thedocument is out of bounds of the first digital image, that the documentin the first digital image is a long document; generating, based on thedetermination that the document in the first digital image is a longdocument, an alert for the user to capture a set of digital images ofthe document, wherein each digital image of the set of digital images ofthe document is of a different portion of the document; obtaining theset of digital images of the document from the user; generating a seconddigital image of the document based on the obtained set of digitalimages; and performing OCR on the second digital image of the document.16. The non-transitory computer-readable medium of claim 15, whereinobtaining the set of digital images comprises obtaining the set ofdigital images via a live stream captured by a camera.
 17. Thenon-transitory computer-readable medium of claim 15, wherein thecomparison of the font size of text in the document with the font sizerange of the determined document type further comprises determining thefont size of the text in the document is less than a lower bound of thefont size range.
 18. The non-transitory computer-readable medium ofclaim 15, wherein the determining the document type of the document inthe first digital image based on a textual content of the documentfurther comprises: performing OCR on the first digital image todetermine the textual content; and comparing the textual content to aset of text indicative of certain types of documents.
 19. Thenon-transitory computer-readable medium of claim 15, wherein determiningthe at least one part of the document in the first digital image is outof bounds of the first digital image further comprises: segmenting thefirst digital image into: a first set of pixels associated with aforeground of the first digital image; and a second set of pixelsassociated with a background of the first digital image; detecting a setof contours in the segmented first digital image; determining a set ofbounding rectangles based on the set of contours; determining an area ofeach bounding rectangle in the set of bounding rectangles; determiningthe bounding rectangle of the set of bounding rectangles with thelargest area; and determining, for each contour of the set of contours,whether the contour is an open contour or a closed contour, wherein: anopen contour is a bounding rectangle with one or more sides outside ofthe segmented first digital image; and a closed contour is a boundingrectangle with all four sides inside of the segmented first digitalimage.
 20. The non-transitory computer-readable medium of claim 19,wherein the operation further comprises determining that the document inthe first digital image is not a long document based on: a determinationthe document is not out of bounds, wherein the determination thedocument is not out of bounds further comprises: the bounding rectanglewith the largest area corresponding to a closed contour; and thebounding rectangle with the largest area does not touch an edge of thefirst digital image; and a determination the font size of the text inthe document is at a top or above the font size range.